code: def calc_train_trade_data(
      i: int,
      train_starts: list[str],
      train_ends: list[str],
      trade_starts: list[str],
      trade_ends: list[str],
      init_train_data: pd.DataFrame(),
      init_trade_data: pd.DataFrame(),
      date_col: str,
  ) -> tuple[pd.DataFrame(), pd.DataFrame()]:
      train_start = train_starts[i]
      train_end = train_ends[i]
      trade_start = trade_starts[i]
      trade_end = trade_ends[i]
      train_data = init_train_data.loc[
          (init_train_data[date_col] >= train_start)
          & (init_train_data[date_col] < train_end)
      ]
      train_data.index = train_data[date_col].factorize()[0]
      trade_data = init_trade_data.loc[
          (init_trade_data[date_col] >= trade_start)
          & (init_trade_data[date_col] < trade_end)
      ]
      trade_data.index = trade_data[date_col].factorize()[0]
      return train_data, trade_data
  
file_path: FinRL/finrl/meta/data_processors/func.py
imports: from __future__ import annotations
  import copy
  import datetime
  import os
  from datetime import date
  from datetime import timedelta
  from typing import List
  from typing import Tuple
  import numpy
  import pandas
label_desc: Use image node_id 9 for calling a global function with example usage: calc_train_trade_data(i, train_starts, train_ends, trade_starts, trade_ends, init_train_data, init_trade_data, date_col) and returns: train_data,  trade_data